applicable models
parametric
Models with a fixed number of parameters
12 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Coefficient Magnitudes (in Linear Models) | Metric | Architecture/linear Models Paradigm/parametric +2 | Tabular | Coefficient Magnitudes explains a linear model by reading its fitted coefficients. Each coefficient shows how much the... | ||
| Integrated Gradients | Algorithmic | Architecture/neural Networks Architecture/neural Networks/transformer +4 | Image Tabular Text | Integrated Gradients assigns input features scores for a prediction relative to a chosen reference input, called the... | ||
| Layer-wise Relevance Propagation | Algorithmic | Requirements/model Internals Architecture/neural Networks +3 | Any | Layer-wise Relevance Propagation (LRP) explains a neural network's prediction by working backwards from the output to... | ||
| Influence Functions | Algorithmic | Architecture/linear Models Architecture/neural Networks +5 | Any | Influence functions estimate which training examples most affected a model's prediction on a particular test example.... | ||
| Federated Learning | Algorithmic | Architecture/linear Models Architecture/neural Networks +4 | Any | Federated learning trains one shared model across several parties, such as phones or hospitals, without collecting their... | ||
| Homomorphic Encryption | Algorithmic | Architecture/linear Models Architecture/model Agnostic +5 | Any | Homomorphic encryption lets a server compute on data that stays encrypted, so the server never sees the underlying... | ||
| Quantile Regression | Algorithmic | Architecture/linear Models/regression Architecture/neural Networks +4 | Tabular | Quantile regression predicts chosen percentiles of an outcome, such as the 10th, 50th and 90th, rather than only its... | ||
| Deep Ensembles | Algorithmic | Architecture/neural Networks Paradigm/parametric +2 | Any | Deep ensembles estimate how uncertain a neural network's predictions are by training several copies of the same network... | ||
| Generalized Additive Models | Algorithmic | Architecture/linear Models/gam Paradigm/parametric +2 | Tabular | A generalised additive model (GAM) builds its prediction by adding a baseline and one smooth curve for each input... | ||
| Exponentiated Gradient Reduction | Algorithmic | Architecture/model Agnostic Paradigm/discriminative +4 | Any | Exponentiated gradient reduction trains a classifier that satisfies a chosen fairness criterion, such as demographic... | ||
| Learning Adversarially Fair and Transferable Representations | Algorithmic | Architecture/neural Networks Paradigm/parametric +4 | Image Tabular | Learning Adversarially Fair and Transferable Representations (LAFTR) trains an encoder that turns each record into a... | ||
| Differentially Private Stochastic Gradient Descent | Algorithmic | Architecture/neural Networks Paradigm/parametric +2 | Any | Differentially private stochastic gradient descent (DP-SGD) trains a neural network, or another model fitted by gradient... |
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